{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import display_html, Image, FileLink, FileLinks, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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6FnkkdowqKqgksSAACTxX7UEjBAPPXp7V95whVzGrharxcpN8ytzX2t5n8/8A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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "image/jpeg": {
       "width": "100px"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='/tmp/dongxi.jpg', width='100px')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href='rich-display.ipynb' target='_blank'>rich-display.ipynb</a><br>"
      ],
      "text/plain": [
       "/home/vagrant/web_develop/chapter12/section2/rich-display.ipynb"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FileLink('rich-display.ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<th>Header 1</th>\n",
       "<th>Header 2</th>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>row 1, cell 1</td>\n",
       "<td>row 1, cell 2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>row 2, cell 1</td>\n",
       "<td>row 2, cell 2</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"\"\"<table>\n",
    "<tr>\n",
    "<th>Header 1</th>\n",
    "<th>Header 2</th>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>row 1, cell 1</td>\n",
    "<td>row 1, cell 2</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>row 2, cell 1</td>\n",
    "<td>row 2, cell 2</td>\n",
    "</tr>\n",
    "</table>\"\"\"\n",
    "HTML(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>gameNum</th>\n",
       "      <th>gameID</th>\n",
       "      <th>teamID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>GP</th>\n",
       "      <th>startingPos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>NLS195507120</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaronha01</td>\n",
       "      <td>1956</td>\n",
       "      <td>0</td>\n",
       "      <td>ALS195607100</td>\n",
       "      <td>ML1</td>\n",
       "      <td>NL</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID  yearID  gameNum        gameID teamID lgID  GP startingPos\n",
       "0  aaronha01    1955        0  NLS195507120    ML1   NL   1        None\n",
       "1  aaronha01    1956        0  ALS195607100    ML1   NL   1        None"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "\n",
    "con = sqlite3.connect(\"/tmp/baseball.sqlite\")\n",
    "df = pd.read_sql(\"select * from allstarfull limit 2\", con)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'<div>\\n<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr style=\"text-align: right;\">\\n      <th></th>\\n      <th>playerID</th>\\n      <th>yearID</th>\\n      <th>gameNum</th>\\n      <th>gameID</th>\\n      <th>teamID</th>\\n      <th>lgID</th>\\n      <th>GP</th>\\n      <th>startingPos</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>0</th>\\n      <td>aaronha01</td>\\n      <td>1955</td>\\n      <td>0</td>\\n      <td>NLS195507120</td>\\n      <td>ML1</td>\\n      <td>NL</td>\\n      <td>1</td>\\n      <td>None</td>\\n    </tr>\\n    <tr>\\n      <th>1</th>\\n      <td>aaronha01</td>\\n      <td>1956</td>\\n      <td>0</td>\\n      <td>ALS195607100</td>\\n      <td>ML1</td>\\n      <td>NL</td>\\n      <td>1</td>\\n      <td>None</td>\\n    </tr>\\n  </tbody>\\n</table>\\n</div>'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df._repr_html_()  # pandas 已经添加了`_repr_html_`方法, 所以能显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><td>a</td><td>1</td></tr><tr><td>c</td><td>4</td></tr><tr><td>b</td><td>2</td></tr></table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class DictTable(dict):\n",
    "    def _repr_html_(self):\n",
    "        html = ['<table>']\n",
    "        for key, value in self.iteritems():\n",
    "            html.append(\"<tr><td>{0}</td><td>{1}</td></tr>\".format(key, value))\n",
    "        html.append(\"</table>\")\n",
    "        return ''.join(html)\n",
    "    \n",
    "t = DictTable(a=1, b=2, c=4)\n",
    "display_html(t)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "widgets": {
   "state": {},
   "version": "1.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
